Computational learning systems driven by the success of deep learning have obtained great success in several computational data mining and learning system as computer vision, natural language processing, clustering, and many more [1]}. However, although deep models have demonstrated promising results on unvarying data, they are susceptible to catastrophic forgetting when applied to dynamic settings, i.e., new information overwrites past experiences, leading to a significant drop in performance of previous tasks.
